The vast majority of individual susceptibility to sickness and disease in humans is generated by complex interactions between multiple loci and the environment, yet we still have very little understanding of how the map between genotype and phenotype is structured or how this structure influences the response to the environment and long term evolutionary change. In particular, complex genetic networks should generate pleiotropic relationships among traits whose functional coupling can make individual effects difficult to examine using traditional knockout approaches. The stress response network, as best elucidated in the nematode Caenorhabditis elegans, is an exemplar of this kind of system because environmentally induced modulation of 1000's of genes is regulated by a set of critical pathways that terminate in a small number of transcription factors. This observation has led to two major hypotheses that have become central to the field: (1) that all stress response phenotypes are pleiotropically coupled and (2) that variation in stress resistance drives variation in longevity. Here, we propose to capitalize on the fundamental discoveries regarding stress response pathways that have been made using C. elegans by using comprehensive functional genomic approaches within the closely related species C. remanei, which is much better suited for studies of natural variation. We aim to (1) determine the regulatory network structure of natural variation in stress response and longevity via the comprehensive mapping of stress phenotypes and the molecular function of critical components of the stress response regulatory network, (2) determine patterns of absolute and flexible connectivity among related stress pathways using experimental evolution under factorial combinations of high oxidative and heat stress, and (3) test functional hypotheses regarding specific gene action and general network structure using natural alleles and by abrogating gene function via RNAi and gene knockouts. We will combine features of each of these approaches using a Bayesian analysis to infer the regulatory structure of the stress response network and to then directly test these predictions using genetic and functional manipulations. The pleiotropy hypotheses will be evaluated indirectly via mapping the propagation of genetic variation across the network and directly via the correlated response of one stress phenotype to selection on another. This research uses a natural systems genetics approach that integrates an understanding of natural genetic variation within a strong functional hypothesis-testing framework to understand the function and evolution of a complex regulatory system with critical implications for human health.